The performance of a gas turbine is subjected to deterioration due to compressor fouling and corrosion, inlet filter clogging, thermal fatigue, and oxidisation of hot gas path components. The performance deterioration results in loss in power output and/or increase in fuel consumption and impacts both revenues and equipment life cycle costs.
The performance degradation attributed to compressor fouling is mainly due to deposits formed on the blades of the first compressor stages by particles carried in by the air that are not large enough to be blocked by the inlet filter. These particles may comprise sludge or pollen in rural areas, dust, rust and soot particles or hydrocarbon aerosols in industrials areas, salt in coastal areas or simply water droplets. The deposits result in a reduction of compressor mass flow rate, efficiency, and pressure ratio. As about half of the energy contained in the fuel burned by the gas turbine is consumed by the compressor, a noticeable increase in fuel consumption has to be accepted in order to maintain a constant power output.
Compressor fouling is a recoverable degradation that can be alleviated by periodic on-line or off-line compressor washes. In an on-line wash, distilled or at least demineralised water is injected into the compressor while the gas turbine is running. Complete performance recovery can only be achieved by an off-line wash (requiring plant shutdown) where distilled water, together with a detergent, is sprayed into the gas turbine and stays in contact with the compressor blades and vanes. Currently, the washing schedule is made manually by the utility operator and the washing is typically scheduled in connection with other planned shutdowns. Alternatively, the washing is scheduled in the slack periods, when the revenues from electricity sales are low.
Inlet filter clogging reduces the gas turbine air flow and compressor inlet pressure and thus adversely affects gas turbine performance. Replacing the old filter with a new or cleaned one can recover the lost performance. However the performance degradation associated with frictional wear and/or concerning hot gas path components is referred to as non-recoverable, the only remedy being an engine overhaul.
In the article “Real Time On-Line Performance Diagnostics of Heavy-Duty Industrial Gas Turbines” by S. C. Gülen et al., Journal of Engineering for Gas Turbines and Power, October 2002, Vol. 124, p. 910-921, a maintenance schedule for the compressor washing and inlet filter replacement balances the maintenance costs against lost revenue and extra fuel costs. The optimal future time to do the washing is found when the integrated cost due to compressor fouling (extra fuel burned and power lost) equals the costs for the maintenance process. However, neither the evolution of the fuel price in the near future, nor logical constraints such as planned outages and part load, are taken into account.
Compressor maps are graphical representations of functions or functional relationships relating e.g. the mass flow of a working fluid through a compressor or turbine and/or the efficiency of the compressor or turbine process to measured or estimated process states such as temperatures, pressures or speeds. Generally, the manufacturers of the compressors or turbines have sufficient experimental data to estimate e.g. the non-recoverable efficiency degradation, also known as “guarantee curve”. On the other hand, the turbine operator himself can approximate or estimate the non-recoverable efficiency ηE, e.g. by means of an interpolation of a particular process state recorded at the restart following a limited number of off-line washing events.
In the European Patent Application 02405844.8 a method based on State Augmented Extended Kalman Filtering techniques is disclosed which allows to obtain and continuously update estimates of the above compressor maps or functions on-line, i.e. during process operation. In the Kalman Filter, the computed output is compared with the measured outputs and the actual (i.e. taking into account recoverable degradation) efficiency η as an augmented state (parameter estimate) is updated. One important aspect in this procedure is the correction of all measured data to standard temperature and pressure conditions for dry air. Despite of the fact that the load level may change several times in-between two washing events, the foregoing procedures generally assume the turbine to work constantly on full or base load and do not take into account part load.
In the absence of any maintenance action, both the actual efficiency η and the estimated efficiency ηE follow an exponential law trend in output degradation. An eventual saturation or levelling off is assumed to be due to the stabilization of the thickness and shape of the blade deposits.
More generally, any system comprising engines or other pieces of equipment that suffer from a continuous degradation in efficiency can be at least temporarily relieved by maintenance actions. However, the scheduling of the latter is not a straightforward task if time-dependent constraints influence on the optimal timing. This is the case in a system that converts a resource into a product where both the resource and the product are each attached different time-dependent properties. These properties are normalized and equivalent to an objective quantity per unit of measure. In the case of a gas turbine as outlined above, the normalized properties are costs or prices per unit of mass or energy (i.e. per kg or per MWh) for the fuel and the electricity generated.
In a different case, the system may comprise a generator and other equipment for producing electrical energy from renewable energy sources such as the sun, wind or water, which are all intermittent or time-dependent by nature. The normalized properties in this case are the natural power, i.e. the energy per unit of time delivered by the resource, and the electrical power produced according to a demand by one or a plurality of consumers.
Because of the decreasing efficiency and the time-dependent normalized properties, the objective quantity introduced above is the subject of a certain balance between the resource end and the product end in the conversion process. There is generally a difference between the amount of the objective quantity entering the system and the amount leaving the system. Correspondingly, the objective quantity may accumulate at the system, or equally, be diverted and used for other purposes than the basic conversion.